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All Journal Jurnal Simetris Jurnal technoscientia Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal Sistem Informasi dan Bisnis Cerdas Register: Jurnal Ilmiah Teknologi Sistem Informasi Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Indonesian Journal of Information System Jurnal Eksplora Informatika JMM (Jurnal Masyarakat Mandiri) JurTI (JURNAL TEKNOLOGI INFORMASI) Compiler CARADDE: Jurnal Pengabdian Kepada Masyarakat Voice Of Informatics Jurnal Teknologi Sistem Informasi dan Aplikasi Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat JMAI (Jurnal Multimedia & Artificial Intelligence) JUSIM (Jurnal Sistem Informasi Musirawas) Antivirus : Jurnal Ilmiah Teknik Informatika Building of Informatics, Technology and Science Technologia: Jurnal Ilmiah Journal of Information Systems and Informatics Jurnal Sistem informasi dan informatika (SIMIKA) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Information Technology Ampera Journal of Computer and Information Systems Ampera Journal of Software Engineering Ampera Charity : Jurnal Pengabdian Masyarakat Prosiding SNAST Jurnal Pengabdian Masyarakat Indonesia Jurnal Ilmiah Teknologi Informasi dan Robotika Jurnal Abdi Masyarakat Indonesia International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sains dan Teknologi Jurnal Pengabdian Mitra Masyarakat (JPMM) Journal of Computer Science and Technology (JCS-TECH) Journal Of Information System And Artificial Intelligence Jurnal Sistem Informasi dan Bisnis Cerdas Journal Research of Social Science, Economics, and Management SisInfo : Jurnal Sistem Informasi dan Informatika Innovative: Journal Of Social Science Research SENAPAS Jurnal INFOTEL SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan International Journal of Artificial Intelligence and Science
Prasetyaningrum, Putri Taqwa
Universitas Mercu Buana Yogyakarta
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Sistem Pakar Pengidentifikasian Jenis Kulis Wajah Dalam Pemilihan Msglow Series Menggunakan Naïve Bayes Hasnidar, Hasnidar; Prasetyaningrum, Putri Taqwa
Jurnal Sains dan Teknologi (JSIT) Vol. 2 No. 2 (2022): Mei - Agustus
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v2i3.204

Abstract

There are still many problems with choosing treatment products that do not match the needs of their skin and skin type. Some people follow what their friends do or what is popular now, and numerous people still try to figure out their skin problems and types without first talking to an expert. Using MS GLOW Makassar as a case study, this research was done based on a needs history. The Naive Bayes method was used to make this system. The goal of designing a skincare selection expert system was to apply and use an expert system so that experts and non-experts could use it to choose skincare based on skin type. Based on 30 costumer data that the system and experts have tested by applying Naive Bayes, the accuracy rate was 100%
Deteksi Leukemia Limfoblastik Akut menggunakan Convolutional Neural Network Akbar, Mutaqin; Prasetyaningrum, Putri Taqwa; Setyaningsih, Putry Wahyu; Ahsan, Moh; Budianto, Alexius Endy
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i1.34168

Abstract

Acute lymphoblastic leukemia is the most important type of childhood leukemia, and accounts for 25% of childhood cancers. Accurately differentiating normal cell precursors from cancer cells is key to the diagnosis of acute lymphoblastic leukemia (ALL). However, under a microscope, cancer cells are so similar to normal cells that it is difficult to classify them. This article presents a detection of acute lymphoblastic leukemia using Convolutional Neural Network (CNN). The dataset which is obtained from ALL_IDB is 582 color image data which is divided into 482 training image data and 100 testing image data. The image data will be resized to 128x128x3 before being input to the CNN model. The CNN model used in this study is a multi-scale CNN which consists of 3 convolution layers (filter size of 3x3, number of filters for each convolution layer is 32, 64, and 128 respectively, and ReLU activation function), 3 subsampling layers using maxpool with filter size of 2x2 , 1 concatenate layer is used to combine the output of each subsampling layer, 1 fully-connected layer with a softmax activation function and a cross-entropy error function, and finally an output layer with 2 classes, namely normal cells and cancer cells. The CNN model will be trained using the Adam optimizer training algorithm with a training rate of 0.0002 and iterated 20 times. Based on the training results after iterating 20 times, the smallest error value was obtained, namely 0.0001 and the largest accuracy value, namely 100% in the 20th epoch. The CNN model was then tested with 100 testing image data and obtained an accuracy rate of 98% and an error value of 0.0482.
Analisis Perbandingan Metode Certainty Factor Dan Dempster Shafer Theory Pada System Pakar Untuk Mendeteksi Penyakit Virus Parechovirus Pada Balita Azzahra, Bernica; Prasetyaningrum, Putri Taqwa
Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research (Special Issue)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i3.12630

Abstract

Riset berikut tujuannya guna mengembangkan sistem pakar guna mendeteksi penyakit virus parechovirus dengan memakai perbandingan 2 metode yaitu Certainty Factor dan Dempster Shafer Theory pada pemberian solusi yang akurat. Penyakit virus Parechovirus ialah virus yang sangat berbahaya khususnya bagi balita dibawah usia 3 bulan. untuk mengatasi masalah ini pengembangan sebuah sistem pakar dirasa tepat dan efisien dalam mendiagnosis penyakit virus parechovirus. Masalah yang di hadapi meliputi tingkat kompleksitas dalam mendiagnosa dan memberikan solusi dengan tingkat kepercayaan yang tinggi. Hasil riset memaparkan bahwasanya kedua metode bisa memberi hasil yang cukup memuaskan, tetapi perbandingan kedua metode itu memberi tambahan wawasan dalam mengartikan keakuratan dan keterbatasan pengembangan sistem pakar. Hasil analisis memaparkan bahwasanya metode teori Dempster-Shafer memberi akurasi yang lebih tinggi dibanding certainty factor. Sebaliknya metode certainty factor mempunyai akurasi yang lebih rendah. Namun riset berikut juga memaparkan bahwasanya hasil maksimal bisa dicapai melalui penggabungan kedua metode ini. Oleh karenanya, riset berikut berkontribusi bagi pengembangan sistem pakar guna mendeteksi penyakit parechoviral dan memberi dasar guna keberlanjutan pengembangan di bidang ini. Kesimpulannya, penggabungan metode Certainty Factor dan Demster-Shafer telah memaparkan potensi guna mengoptimalkan keandalan dan kinerja sistem pakar untuk secara efektif menunjang diagnosis penyakit parechovirus. Hasil perhitungan kedua metode memaparkan bahwasanya metode Dempster-Schaefer memiliki taraf kepercayaan senilai 99,77% dan metode Certainty Factor memiliki taraf kepercayaan senilai 54,22%.
Forward Chaining Method in a Web-Based Bipolar Disorder Expert System Aritonang, Roselina; Prasetyaningrum, Putri Taqwa
Compiler Vol 12, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v12i2.1694

Abstract

Bipolar disorder is a psychiatric disorder. At Argodadi Pinilih Disability Secretariat, Sedayu District, Yogyakarta Region, around 60% of people with disabilities are affected by psychiatric disorders and one of them is bipolar, one of them is depression, and suicide if initial treatment is not given to the sufferer. In addition, the cost of doing consultation with the medical specialist is not cheap.An expert system is an artificial intelligence system that is useful for diagnosing an error and as a decision-maker with the knowledge rules applied by an intelligent system that can solve problems like an expert. In making an expert system, the forward chaining method is used which aims to be able to diagnose bipolar disorder with accurate results and its utilization can be used by experts and laypeople to make an initial diagnosis of bipolar disorder.The results of this study are in the form of an expert system program that is used to diagnose bipolar disorder which can provide information related to the disease and can provide information on initial treatment of the disease. The information obtained by the consultant from the expert system is in the form of a percentage.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN SUPPLIER BUKU PADA TOGAMAS MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) Karlina, Leni; Prasetyaningrum, Putri Taqwa
JMAI (Jurnal Multimedia & Artificial Intelligence) Vol. 4 No. 2 (2020): JMAI (Jurnal Multimedia & Artificial Intelligence)
Publisher : LPPM Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A selection of the best books’ supplier is very important in a bookstore, in producing books for their store. Sometimes, several books suppliers are difficult to choose, because many books suppliers from many regions, in which the selection is still manual. Such way causes a decision making which needs an accurate and precise calculation among the existed books suppliers. Therefore, researchers designed a decision support system for books suppliers’ selection, to be used by the TOGAMAS Bookstore in determining the books suppliers which will easily be selected, based on the specified criteria. This decision support system was created using the method of Simple Additive Weighting (SAW) as the tool that would help the TOGAMAS. The test was done by comparing the calculation from the TOGAMAS, with the calculation from the SAW, using 10 suppliers’ data to test the system performance. Based on the test, it was concluded that the percentage of the system performance was 50%.
Analisis Kinerja Model Support Vector Machine dalam Prediksi Kasus HIV di Indonesia Berdasarkan Data Time Series Erza, Muhammad Al-Ghifari; Prasetyaningrum, Putri Taqwa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7365

Abstract

Accurate predictions of HIV cases are crucial in efforts to control the epidemic effectively in Indonesia. As the number of cases and the complexity of transmission factors increase, machine learning-based prediction methods are becoming increasingly relevant. This study analyzes the performance of the Support Vector Machine (SVM) model in forecasting the number of HIV cases in Indonesia using time series data from 2012 to 2024. The CRISP-DM methodology is used as the framework for the analysis process, starting from business understanding to model deployment. The dataset used includes secondary data from the Ministry of Health, such as SIHA, national surveillance, and reports from the Directorate General of Disease Prevention and Control (Ditjen P2P). The SVM model is selected due to its ability to handle non-linear data and limited data sizes, as well as its resilience to overfitting. Model evaluation is performed using MAE, RMSE, and MAPE metrics. The results of the study show that the SVR model with an RBF kernel provides good prediction accuracy, with MAE values of 691.34, RMSE of 823.11, and MAPE of 13% on the test data. Therefore, SVM can be an effective tool to support data-driven decision-making in HIV control efforts in Indonesia.
Segmentasi Produk Pakaian Menggunakan Algoritma K-Means Clustering dan Particle Swarm Optimization untuk Strategi Pemasaran Putra, Rio Aji Hadyanta; Prasetyaningrum, Putri Taqwa
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7367

Abstract

This research aims to analyze product segmentation in the apparel industry using the K-Means Clustering algorithm optimized with Particle Swarm Optimization (PSO) to generate accurate product segmentation that can support more effective marketing strategies for a company. The data used in this analysis were obtained from sales transactions of a clothing manufacturing company that offers various categories of apparel products. The dataset consists of 333 rows and includes transaction numbers, product types, quantities sold, and total sales values. The data were processed using the Python programming language via Visual Studio Code. The segmentation process was initially performed using the K-Means algorithm to group products, and the Elbow method was applied to determine the optimal number of clusters. The number of clusters obtained from the Elbow method was then optimized using PSO to find more optimal cluster counts and centroids. Cluster evaluation was conducted by comparing the values of several metrics, including the Davies-Bouldin Index (DBI), Silhouette Score, Sum of Squared Error (SSE), and the SSW/SSB ratio. Although the DBI increased slightly from 0.6690 to 0.6878, indicating greater similarity between clusters, the improvement in the Silhouette Score from 0.5513 to 0.5569 suggests better internal consistency within the clusters. Furthermore, the reduction in SSE from 418.52 to 313.25 indicates a tighter distribution of data within clusters, while the significant decrease in the SSW/SSB ratio from 0.4582 to 0.3075 demonstrates more clearly defined cluster boundaries and improved separation. The results of the study produced four distinct product clusters, enabling the company to implement more targeted and differentiated marketing strategies.
Optimizing Arduino-Based Laser Cut Machine Settings for Home Industry Subagyo, Ibnu Rivansyah; Prasetyaningrum, Putri Taqwa
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.3

Abstract

The rapid development of laser technology has significantly impacted various industrial sectors, particularly through the use of CNC laser cutting machines. These machines offer distinct advantages and limitations, making them suitable for processing a wide range of materials. This study aims to identify the most effective and efficient settings for a diode-based CNC laser cutting machine, specifically for cutting plywood. An experimental approach was employed, involving the design, creation, and testing of the machine. The research focused on optimizing the focus point and operational settings to achieve precise cuts. The results indicate that the optimal focus point is 12.6 mm, with the best cutting performance achieved at a speed of 500 mm/min, 30% laser power, and 7 passes. The findings suggest that this CNC laser machine is highly efficient for small-scale industries, offering affordability, ease of production, and reduced labor costs by automating multiple machines with a single computer. However, its application is limited in large-scale manufacturing due to constraints related to the Arduino-based control system and the maximum work area size.
Co-Authors Adi Ronggo Wicaksono Affandi Putra Pradana Agung Supoyo Agustin, Isnaini Ahmad Iwan Fadli Ahmad Mukhlasin Ahsan, Moh Ajisari, Lanang Dian Albert Yakobus Chandra Albert Yakobus Chandra Alphi Mukti Anggie Kurniawati Anggo Luthfi Yunanto Ari Wibowo Arita Witanti Aritonang, Roselina Artika Sari Arwa Ulayya Haspriyanti Ati, Gresensia Rosadelima Azzahra, Bernica Bagus Nur Solayman Bambang Setio Purnomo Bambang Setio Purnomo Budianto, Alexius Endy Cindy Okta Melinda Dapit Virdaus Denny Jean Cross Sihombing Devi Febrianti dewi, Ine shinta Dhana Sudana Eka Aryani, Eka Erza, Muhammad Al-Ghifari Fransiskus Xaverius Pere GUNARTATIK ESTHININGTYAS Hamam Nurrofiq Hasnidar Hasnidar Heri Agus Prasetyo Herin, Sofia Ibnu Rivansyah Subagyo Ibrahim, Norshahila Irfan Pratama Irya Wisnubhadra Julius Bata Jumiyati Juwita Juwita Karlina, Leni Khalifah Samiih Sya'bani Sya'bani Khoirut Tamimi Kris Rahayu Kristina Andryani Larasaty, Raditha Latifah, Retno Leni Karlina Lewoema, Scholastica Larissa Zefira luky kurniawan, luky M. Anjas Leonardi M. Irfan Bahri Mita Oktafani Mu'ti, Dewi Lestari Mukti, Alphi Rinaldi Nalendra Mutaqin Akbar Nadeak, Puja Waldi Nanda, Tietan Geovanka Ningsih, Rully Ningsih, Ruly Norshahila Ibrahim Nuning Rusmilawati Nur Sholehah Dian Saputri Nuri Budi Hangesti Nurul Tiara Kadir Okta, Sri Oktafani, Mita Ozzi Suria Ozzi Suria Ozzi Suria Pipin Yuliyanto Pratama, Bagus Wahyu Ari Pratama, Harfin Ibna Pratama, Irfan Puja Waldi Nadeak Puja Putra, Rio Aji Hadyanta Putry Wahyu Setyaningsih Rani Dwi Lestari Reny Yuniasanti Resi Dwi Febrianti Rias Ilham Agung Nugroho Rustiawan, Muhammad Rizqi Akfani saka, Hildegardis Kristina Santoso Pamungkas Sari, Artika Scholastica Lewoema Setiyani, Santi Setyaningsih, Putry Wahyu Simarmata, Penni Wintasari Subagyo, Ibnu Rivansyah Suria, Ozzi Suyoto Suyoto Viony Julianti Sipayung Wahyuningsih Wahyuningsih